Patient-Specific Modelling in Orthopedics: From Image to Surgery

  • G. T. Gomes
  • S. Van Cauter
  • M. De Beule
  • L. Vigneron
  • C. Pattyn
  • E. A. Audenaert
Chapter
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 4)

Abstract

In orthopedic surgery, to decide upon intervention and how it can be optimized, surgeons usually rely on subjective analysis of medical images of the patient, obtained from computed tomography, magnetic resonance imaging, ultrasound or other techniques. Recent advancements in computational performance, image analysis and in silico modeling techniques have started to revolutionize clinical practice through the development of quantitative tools, including patient specific models aiming at improving clinical diagnosis and surgical treatment. Anatomical and surgical landmarks as well as features extraction can be automated allowing for the creation of general or patient specific models based on statistical shape models. Preoperative virtual planning and rapid prototyping tools allow the implementation of customized surgical solutions in real clinical environments. In the present chapter we discuss the applications of some of these techniques in orthopedics and present new computer-aided tools that can take us from image analysis to customized surgical treatment.

Keywords

Musculoskeletal modelling Patient-specific models Surgical planning 

References

  1. Ahn DG, Lee JY, Yang DY (2006) Rapid prototyping and reverse engineering application for orthopedic surgery planning. J Mech Sci Technol 20(1):19–28CrossRefGoogle Scholar
  2. Anaya AM, Vigneron L, Diab M, Burch S (2011) Evaluation of virtual planning, rapid prototyping modeling, and image-guided navigation in periacetabular osteotomy. In: Proceedings of computer assisted radiology and surgery 2011, S111Google Scholar
  3. Audenaert EA, Baelde N, Huysse W, Vigneron L, Pattyn C (2010) Development of a three-dimensional detection method of cam deformities in femoroacetabular impingement. Skeletal Radiol 40:921–927CrossRefGoogle Scholar
  4. Audenaert E, Vigneron L, Pattyn C (2011) A method for three-dimensional evaluation and computer aided treatment of femoroacetabular impingement. Comput Aided Surg 16:143–148CrossRefGoogle Scholar
  5. Bagaria V, Deshpande S, Rasalkar DD, Kuthe A, Paunipagar BK (2011) Use of rapid prototyping and three-dimensional reconstruction modeling in the management of complex fractures. Eur J Radiol 80(3):814–820. Epub 2011 Jan 22. Review. http:/pubmed/21256690 CrossRefGoogle Scholar
  6. Baldwin MA, Langenderfer JE, Rullkoetter PJ, Laz PJ (2010) Development of subject specific and statistical shape models of the knee using an efficient segmentation and mesh-morphing approach. Comput Methods Programs Biomed 97:232–240CrossRefGoogle Scholar
  7. Barratt DC, Chan CS, Edwards PJ, Penney GP, Slomczykowski M, Carter TJ et al (2008) Instantiation and registration of statistical shape models of the femur and pelvis using 3D ultrasound imaging. Med Image Anal 12:358–374CrossRefGoogle Scholar
  8. Behiels G, Maes F, Vandermeulen D, Suetens P (2002) Evaluation of image features and search strategies for segmentation of bone structures in radiographs using active shape models. Med Image Anal 6:47–62CrossRefGoogle Scholar
  9. Benameur S, Mignotte M, Parent S, Labelle H, Skalli W, de Guise J (2003) 3D/2D registration and segmentation of scoliotic vertebrae using statistical models. Comput Med Imag Graph 27:321–337CrossRefGoogle Scholar
  10. Birnbaum K, Schkommodau E, Decker N, Prescher A, Klapper U, Radermacher K (2001) Computer-assisted orthopedic surgery with individual templates and comparison to conventional operation method. Spine 26:365–370CrossRefGoogle Scholar
  11. Blanz V, Mehl A, Vetter T, Seidel HP. (2004) A statistical method for robust 3D surface reconstruction from sparse data. In: International symposium on 3D data processing, visualization and transmission, Thessaloniki, Greece, pp 293–300Google Scholar
  12. Blemker SS, Asakawa DS, Gold GE, Delp SL (2007) Image-based musculoskeletal modeling: applications, advances, and future opportunities. J Magn Reson Imaging 25(2):441–451CrossRefGoogle Scholar
  13. Bookstein FL (1997) Landmark methods for forms without landmarks: morphometrics of group differences in outline shape. Med Image Anal 1(3):225–243CrossRefGoogle Scholar
  14. Brown GA, Firoozbakhsh K, DeCoster TA, Reyna JR Jr, Moneim M (2003) Rapid prototyping: the future of trauma surgery? J Bone Joint Surg 85(4):49–55Google Scholar
  15. Bryan R, Nair PB, Taylor M (2009) Use of a statistical model of the whole femur in a large scale, multi-model study of femoral neck fracture risk. J Biomech 42:2171–2176CrossRefGoogle Scholar
  16. Cerveri P, Marchente M, Bartels W, Corten K, Simon JP, Manzotti A (2010) Automated method for computing the morphological and clinical parameters of the proximal femur using heuristic modeling techniques. Ann Biomed Eng 38(5):1752–1766CrossRefGoogle Scholar
  17. Cootes TF, Taylor CJ (2004) Anatomical statistical models and their role in feature extraction. Br J Radiol 77:S133–S139CrossRefGoogle Scholar
  18. Cootes TF, Taylor CJ, Cooper D, Graham J (1995) Active shape models – their training and application. Comput Vis Image Underst 61:38–59CrossRefGoogle Scholar
  19. Cootes TF, Taylor CJ, Manchester M Pt (2004) Statistical models of appearance for computer vision. (Online: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.1455)
  20. Delaunay S, Dussault RG, Kaplan PA, Alford BA (1997) Radiographic measurements of dysplastic adult hips. Skeletal Radiol 26(2):75–81CrossRefGoogle Scholar
  21. Delp SL, Loan JP (1995) A graphics-based software system to develop and analyze models of musculoskeletal structures. Comput Biol Med 25(1):21–34CrossRefGoogle Scholar
  22. Delp SL, Anderson FC, Arnold AS, Loan P, Habib A, John CT, Guendelman E, Thelen DG (2007) OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans Biomed Eng 54:1940–1950CrossRefGoogle Scholar
  23. Dryden IL, Mardia K (1998) Statistical shape analysis. Wiley, ChichesterMATHGoogle Scholar
  24. Ehrhardt J, Handels H, Plötz W, Pöppl SJ (2004) Atlas-based recognition of anatomical structures and landmarks and the automatic computation of orthopedic parameters. Methods Inf Med 43(4):391–397Google Scholar
  25. Fleute M, Lavallée S (1998) Building a complete surface model from sparse data using statistical shape model. Lect Notes Comput Sci 1496:879–887CrossRefGoogle Scholar
  26. Gomes GT (2011) Automatic feature extraction and statistical shape analysis of the Femur. Submitted to computer methods in biomechanics and biomedical engineeringGoogle Scholar
  27. Grood ES, Suntay WJ (1983) A joint coordinate system for the clinical description of three-dimensional motions: application to the knee. J Biomech Eng 105(2):136–144CrossRefGoogle Scholar
  28. Hafez MA, Chelule KL, Seedhom BB, Sherman KP (2006) Computer-assisted total knee arthroplasty using patient-specific templating. Clin Orthop Relat Res 444:184–192CrossRefGoogle Scholar
  29. Hananouchi T, Saito M, Koyama T, Hagio K, Murase T, Sugano N, Yoshikawa H (2009) Tailor-made surgical guide based on rapid prototyping technique for cup insertion in total hip arthroplasty. Int J Med Robot 5(2):164–169CrossRefGoogle Scholar
  30. Hankemeier S, Gosling T, Richter M, Hufner T, Hochhausen C, Krettek C (2006) Computer-assisted analysis of lower limb geometry: higher intraobserver reliability compared to conventional method. Comput Aided Surg 11(2):81–86Google Scholar
  31. Heimann T, Meinzer HP (2009) Statistical shape models for 3D medical image segmentation: a review. Med Image Anal 13:543–563CrossRefGoogle Scholar
  32. Hopkinson N, Hague R, Dickens P (2005) Rapid manufacturing: an industrial revolution for the digital age. Wiley, ChichesterCrossRefGoogle Scholar
  33. Jolliffe IT (2002) Principal component analysis, 2nd edn. Springer, New YorkMATHGoogle Scholar
  34. Kunz M, Rudan JF, Xenoyannis GL, Ellis RE (2010) Computer assisted hip resurfacing using individualized drill templates. J Arthroplasty 25(4):600–606CrossRefGoogle Scholar
  35. Kurazume R, Nakamura K, Okada T, Sato Y, Sugano N, Koyama T, Iwashita Y, Hasegawa T (2009) 3D reconstruction of a femoral shape using a parametric model and two 2d fluoroscopic images. Comput Vis Image Underst 113(2):202–211CrossRefGoogle Scholar
  36. Lamecker H, Seebass M, Hege HC, Deuflhard P (2004) A 3d statistical shape model of the pelvic bone for segmentation. In: Fitzpatrick JM, Sonka M (eds), SPIE 5370(1):1341–1351Google Scholar
  37. Leong NL, Buijze GA, Fu EC, Stockmans F, Jupiter JB (2010) Computer-assisted versus non-computer-assisted preoperative planning of corrective osteotomy for extra-articular distal radius malunions: a randomized controlled trial. BMC Musculoskelet Disord 11:282CrossRefGoogle Scholar
  38. Lerner AL, Tamez-Pena JG, Houck JR, Yao J, Harmon HL, Salo AD, Totterman SM (2003) The use of sequential MR image sets for determining tibiofemoral motion: reliability of coordinate systems and accuracy of motion tracking algorithm. J Biomech Eng 125(2):246–253CrossRefGoogle Scholar
  39. Lombardi AV, Berend KR, Adams JB (2008) Patient-specific approach in total knee arthroplasty. Orthopedics 31(9):927–930CrossRefGoogle Scholar
  40. Morton NA, Maletsky LP, Pal S, Laz PJ (2007) Effect of variability in anatomical landmark location on knee kinematic description. J Orthop Res 25(9):1221–1230CrossRefGoogle Scholar
  41. Nizard R (2002) Computer assisted surgery for total knee arthroplasty. Acta Orthop Belg 68(3):215–230Google Scholar
  42. Nofrini L, Slomczykowski M, Iacono F, Marcacci M (2004) Evaluation of accuracy in ankle center location for tibial mechanical axis identification. J Invest Surg 17(1):23–29Google Scholar
  43. Oka K, Moritomo H, Goto A et al (2008) Corrective osteotomy for malunited intra-articular fracture of the distal radius using a custom-made surgical guide based on three-dimensional computer simulation: case report. J Hand Surg Am 33:835–840CrossRefGoogle Scholar
  44. Oka K, Murase T, Moritomo H, Goto A, Sugamoto K, Yoshikawa H (2010) Corrective osteotomy using customized hydroxyapatite implants prepared by preoperative computer simulation. Int J Med Robot 6(2):186–193Google Scholar
  45. Paley D (2002) Normal lower limb alignment and joint orientation. In: Paley D, Herzenberg JE (eds) Principles of deformity correction. Springer, New YorkGoogle Scholar
  46. Pattyn C, De Smedt K, Gelaude F, Clijmans T, Dille J, Geebelen B, Audenaert E (2010) A custom-made guide for femoral component positioning in hip resurfacing arthroplasty: development and validation study. J Biomech 43(1)Google Scholar
  47. Raaijmaakers M, Gelaude F, De Smedt K, Clijmans T, Dille J, Mulier M (2010) A custom-made guide-wire positioning device for hip surface replacement arthroplasty: description and first results. BMC Musculoskelet Disord 11:161CrossRefGoogle Scholar
  48. Radermacher K, Portheine F, Anton M, Zimolong A, Kaspers G, Rau G, Staudte HW (1998) Computer assisted orthopaedic surgery with image-based individual templates. Clin Orthop Relat Res 354:28–38CrossRefGoogle Scholar
  49. Rajamani KT, Styner MA, Talib H, Zheng G, Nolte LP, Gonzáles Ballester MA (2007) Statistical deformable bone models for robust 3D surface extrapolation from sparse data. Med Image Anal 11:99–109CrossRefGoogle Scholar
  50. Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ (1999) Non-rigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imag 18:712–721CrossRefGoogle Scholar
  51. Schöttle PB, Schmeling A, Rosenstiel N, Weiler A (2007) Radiographic land-marks for femoral tunnel placement in medial patellofemoral ligament reconstruction. Am J Sports Med 35(5):801–804CrossRefGoogle Scholar
  52. Siston RA, Giori NJ, Goodman SB, Delp SL (2007) Surgical navigation for total knee arthroplasty: a perspective. J Biomech 40(4):728–735CrossRefGoogle Scholar
  53. Stindel E, Briard JL, Merloz P, Plaweski S, Dubrana F, Lefevre C et al (2002) Bone morphing: 3D morphological data for total knee arthroplasty. Comput Aid Surg 7:156–168CrossRefGoogle Scholar
  54. Styner M, Lieberman JA, McClure RK, Weinberger DR, Jones DW, Gerig G (2005) Morphometric analysis of lateral ventricles in schizophrenia and healthy controls regarding genetic and disease specific factors. Proc Natl Acad Sci USA 102(13):4872–4877CrossRefGoogle Scholar
  55. Subburaj K, Ravi B, Agarwal M (2009) Automated identification of anatomical landmarks on 3D bone models reconstructed from CT scan images. Comput Med Imaging Graph 33(5):359–368CrossRefGoogle Scholar
  56. Subburaj K, Ravi B, Agarwal M (2010) Computer-aided methods for assessing lower limb deformities in orthopaedic surgery planning. Comput Med Imaging Graph 34(4):277–288CrossRefGoogle Scholar
  57. Tang TS, Ellis RE (2005) 2D/3D deformable registration using a hybrid atlas. Med Image Comput Comput Assist Interv 8:223–230Google Scholar
  58. Van Cauter S, De Beule M, Van Haver A, Verdonk P, Verhegghe B (2012) Automated extraction of the femoral anatomical axis for determining the intramedullary rod parameters in total knee arthroplasty. Int J Numer Methods Biomed Eng 28(1):158–169Google Scholar
  59. Van Sint Jan S (2007) Color atlas of skeletal landmark definitions. Guidelines for reproducible manual and virtual palpations. Churchill Livingstone–Elsevier, EdinburghGoogle Scholar
  60. Van Sint Jan S, Della Croce U (2005) Identifying the location of human skeletal landmarks: why standardized definitions are necessary-a proposal. Clin Biomech (Bristol, Avon) 20(6):659–660CrossRefGoogle Scholar
  61. Victor J, Van Doninck D, Labey L, Innocenti B, Parizel PM, Bellemans J (2009) How precise can bony landmarks be determined on a CT scan of the knee? Knee 16(5):358–365CrossRefGoogle Scholar
  62. Victor J, Deprez P, Premanathan A, Keppler L (2011) Virtual 3d planning and patient specific surgical guides for osteotomies around the knee. In: Proceedings of Computer Assisted Orthopaedics Surgery. Digital Proceedings so far (CD-ROM). They will be published in JBJS: http://www.bjjprocs.boneandjoint.org.uk/site/includefiles/abstractspending.xhtml
  63. Wolf A, Digioia AM 3rd, Mor AB, Jaramaz B (2005) Cup alignment error model for total hip arthroplasty. Clin Orthop Relat Res 437:132–137CrossRefGoogle Scholar
  64. Wong KC, Kumta SM, Antonio GE, Tse LF (2008) Image fusion for computer-assisted bone tumor surgery. Clin Orthop Relat Res 466(10):2533–2541CrossRefGoogle Scholar
  65. Wong KC, Kumta SM, Leung KS, Ng KW, Ng EW, Lee KS (2010) Integration of CAD/CAM planning into computer assisted orthopaedic surgery. Comput Aided Surg 15(4–6):65–74CrossRefGoogle Scholar
  66. Yang YM, Rueckert D, Bull AMJ (2008) Predicting the shapes of bones at a joint: application to the shoulder. Comput Methods Biomech Biomed Eng 11(1):19–30CrossRefGoogle Scholar
  67. Yoon YS, Hodgson AJ, Tonetti J, Masri BA, Duncan CP (2008) Resolving inconsistencies in defining the target orientation for the acetabular cup angles in total hip arthroplasty. Clin Biomech 23(3):253–259CrossRefGoogle Scholar
  68. Yoshino N, Takai S, Ohtsuki Y, Hirasawa Y (2001) Computed tomography measurement of the surgical and clinical transepicondylar axis of the distal femur in osteoarthritic knees. J Arthroplasty 16(4):493–497CrossRefGoogle Scholar
  69. Zheng G, Schumann S (2009) 3D reconstruction of a patient-specific surface model of the proximal femur from calibrated X-ray radiographs: a validation study. Med Phys 36:1155–1166CrossRefGoogle Scholar
  70. Zheng G, Gonzáles-Ballester MA, Styner M, Nolte LP (2006) Reconstruction of patient specific 3d bone surface from 2d calibrated fluoroscopic images and point distribution model. Lect Notes Comput Sci 4190:25–32CrossRefGoogle Scholar
  71. Zheng G, Gollmer S, Schumann S, Dong X, Feilkas T, Ballester MAG (2008) A 2d/3d correspondence building method for reconstruction of a patient-specific 3d bone surface model using point distribution models and calibrated X-ray images. Med Image Anal 13(6):883–899CrossRefGoogle Scholar
  72. Ziegler CG, Pietrini SD, Westerhaus BD, Anderson CJ, Wijdicks CA, Johansen S, Engebretsen L, Laprade RF (2010) Arthroscopically pertinent landmarks for tunnel positioning in single-bundle and double-bundle anterior cruciate ligament reconstructions. Am J Sports Med 39(4):743–752CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • G. T. Gomes
    • 1
  • S. Van Cauter
    • 2
  • M. De Beule
    • 2
  • L. Vigneron
    • 3
  • C. Pattyn
    • 1
  • E. A. Audenaert
    • 1
  1. 1.Ghent University HospitalGhentBelgium
  2. 2.IBiTech–bioMMedaGhent UniversityGhentBelgium
  3. 3.Orthopedic DepartmentMaterialise NVLeuvenBelgium

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